Overview

Dataset statistics

Number of variables9
Number of observations13703
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory963.6 KiB
Average record size in memory72.0 B

Variable types

NUM9

Warnings

mean_closeness is highly correlated with average_clustering and 1 other fieldsHigh correlation
average_clustering is highly correlated with mean_closeness and 1 other fieldsHigh correlation
assortativity is highly correlated with average_clustering and 1 other fieldsHigh correlation
rg has unique values Unique
shape has unique values Unique

Reproduction

Analysis started2020-11-27 01:59:08.132770
Analysis finished2020-11-27 01:59:22.628910
Duration14.5 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

rg
Real number (ℝ≥0)

UNIQUE

Distinct13703
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4053879859
Minimum0.2647429215
Maximum0.553959391
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:22.719635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.2647429215
5-th percentile0.316142869
Q10.3761514338
median0.4080868467
Q30.4348126528
95-th percentile0.4804880947
Maximum0.553959391
Range0.2892164694
Interquartile range (IQR)0.058661219

Descriptive statistics

Standard deviation0.04856659662
Coefficient of variation (CV)0.1198027527
Kurtosis0.08702454773
Mean0.4053879859
Median Absolute Deviation (MAD)0.03060915531
Skewness-0.02886839327
Sum5555.031571
Variance0.002358714307
MonotocityNot monotonic
2020-11-26T19:59:22.851605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.41661888191< 0.1%
 
0.3195550271< 0.1%
 
0.42793620971< 0.1%
 
0.37751600561< 0.1%
 
0.42365575211< 0.1%
 
0.39345681281< 0.1%
 
0.38560582981< 0.1%
 
0.37203102061< 0.1%
 
0.40161231541< 0.1%
 
0.36017700741< 0.1%
 
Other values (13693)1369399.9%
 
ValueCountFrequency (%) 
0.26474292151< 0.1%
 
0.26630381851< 0.1%
 
0.26689799121< 0.1%
 
0.26742963611< 0.1%
 
0.26744080811< 0.1%
 
ValueCountFrequency (%) 
0.5539593911< 0.1%
 
0.55348004891< 0.1%
 
0.55287079091< 0.1%
 
0.55225306731< 0.1%
 
0.552019421< 0.1%
 

mean_displacement
Real number (ℝ≥0)

Distinct13633
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.36289176
Minimum0
Maximum142.2368066
Zeros71
Zeros (%)0.5%
Memory size107.1 KiB
2020-11-26T19:59:22.985850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.203558696
Q16.592146559
median10.31254505
Q320.77357781
95-th percentile38.04664558
Maximum142.2368066
Range142.2368066
Interquartile range (IQR)14.18143125

Descriptive statistics

Standard deviation11.68200973
Coefficient of variation (CV)0.8133466382
Kurtosis4.302220454
Mean14.36289176
Median Absolute Deviation (MAD)4.869084109
Skewness1.477814807
Sum196814.7058
Variance136.4693512
MonotocityNot monotonic
2020-11-26T19:59:23.117920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0710.5%
 
15.793932121< 0.1%
 
25.302949711< 0.1%
 
8.8598286641< 0.1%
 
10.917192441< 0.1%
 
29.302648921< 0.1%
 
8.4257968321< 0.1%
 
10.161183081< 0.1%
 
6.3995488391< 0.1%
 
8.6414847741< 0.1%
 
Other values (13623)1362399.4%
 
ValueCountFrequency (%) 
0710.5%
 
1.0166660451< 0.1%
 
1.0239161761< 0.1%
 
1.0343061031< 0.1%
 
1.0352389291< 0.1%
 
ValueCountFrequency (%) 
142.23680661< 0.1%
 
119.47645421< 0.1%
 
117.66914261< 0.1%
 
115.45183611< 0.1%
 
105.97171721< 0.1%
 

shape
Real number (ℝ)

UNIQUE

Distinct13703
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004182291781
Minimum-0.06826915628
Maximum0.1877051027
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:23.250898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.06826915628
5-th percentile-0.00569033166
Q1-0.001052393195
median0.0004036715331
Q30.003431179983
95-th percentile0.0319963595
Maximum0.1877051027
Range0.2559742589
Interquartile range (IQR)0.004483573177

Descriptive statistics

Standard deviation0.01661281353
Coefficient of variation (CV)3.972179465
Kurtosis30.47326611
Mean0.004182291781
Median Absolute Deviation (MAD)0.002056363178
Skewness4.472019201
Sum57.30994428
Variance0.0002759855735
MonotocityNot monotonic
2020-11-26T19:59:23.387381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.00034098175651< 0.1%
 
-0.0025577424471< 0.1%
 
-0.0017133150841< 0.1%
 
-0.027132129971< 0.1%
 
0.0095978464011< 0.1%
 
2.308752524e-051< 0.1%
 
0.00021584799061< 0.1%
 
0.0093619541381< 0.1%
 
-0.00023418705251< 0.1%
 
0.0025826134621< 0.1%
 
Other values (13693)1369399.9%
 
ValueCountFrequency (%) 
-0.068269156281< 0.1%
 
-0.063639069681< 0.1%
 
-0.059777669991< 0.1%
 
-0.057552284971< 0.1%
 
-0.055199341571< 0.1%
 
ValueCountFrequency (%) 
0.18770510271< 0.1%
 
0.17869234011< 0.1%
 
0.17557467891< 0.1%
 
0.17554513671< 0.1%
 
0.17376994821< 0.1%
 

density
Real number (ℝ≥0)

Distinct9829
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001444083549
Minimum0.001331047295
Maximum0.001676962743
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:23.520127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.001331047295
5-th percentile0.001361556332
Q10.001403173627
median0.001445711122
Q30.001482920248
95-th percentile0.001533014509
Maximum0.001676962743
Range0.0003459154478
Interquartile range (IQR)7.974662057e-05

Descriptive statistics

Standard deviation5.35448747e-05
Coefficient of variation (CV)0.03707879279
Kurtosis-0.5430892429
Mean0.001444083549
Median Absolute Deviation (MAD)3.944956711e-05
Skewness0.1890523753
Sum19.78827688
Variance2.867053606e-09
MonotocityNot monotonic
2020-11-26T19:59:23.646168image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00135939786390.1%
 
0.00138610075580.1%
 
0.00137711270780.1%
 
0.00141675977370.1%
 
0.00137551141370.1%
 
0.00136551873870.1%
 
0.00135988365970.1%
 
0.00137164204270.1%
 
0.0014275244566< 0.1%
 
0.0013859198236< 0.1%
 
Other values (9819)1363199.5%
 
ValueCountFrequency (%) 
0.0013310472951< 0.1%
 
0.0013314758321< 0.1%
 
0.0013339505071< 0.1%
 
0.0013342174471< 0.1%
 
0.0013345903591< 0.1%
 
ValueCountFrequency (%) 
0.0016769627431< 0.1%
 
0.0016336529611< 0.1%
 
0.0016296851571< 0.1%
 
0.0016285878221< 0.1%
 
0.0016278070591< 0.1%
 

clique_number
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.13084726
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:23.757338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median4
Q34
95-th percentile7
Maximum8
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.141959601
Coefficient of variation (CV)0.2764468231
Kurtosis0.6922155377
Mean4.13084726
Median Absolute Deviation (MAD)1
Skewness1.135683239
Sum56605
Variance1.304071731
MonotocityNot monotonic
2020-11-26T19:59:23.838959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
4625445.6%
 
3423930.9%
 
613009.5%
 
510958.0%
 
77255.3%
 
8570.4%
 
2330.2%
 
ValueCountFrequency (%) 
2330.2%
 
3423930.9%
 
4625445.6%
 
510958.0%
 
613009.5%
 
ValueCountFrequency (%) 
8570.4%
 
77255.3%
 
613009.5%
 
510958.0%
 
4625445.6%
 

average_clustering
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12627
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.153942953
Minimum0
Maximum0.6084742648
Zeros33
Zeros (%)0.2%
Memory size107.1 KiB
2020-11-26T19:59:23.952804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008762151113
Q10.0417315056
median0.06461505922
Q30.1513957745
95-th percentile0.6000244783
Maximum0.6084742648
Range0.6084742648
Interquartile range (IQR)0.1096642689

Descriptive statistics

Standard deviation0.190103778
Coefficient of variation (CV)1.234897566
Kurtosis1.32432533
Mean0.153942953
Median Absolute Deviation (MAD)0.05288079484
Skewness1.693284119
Sum2109.480285
Variance0.03613944639
MonotocityNot monotonic
2020-11-26T19:59:24.075775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0330.2%
 
0.04424778761100.1%
 
0.04879074658100.1%
 
0.0462025316590.1%
 
0.044331855690.1%
 
0.0442757748380.1%
 
0.0488523899870.1%
 
0.044359949370.1%
 
0.048339638570.1%
 
0.0443037974770.1%
 
Other values (12617)1359699.2%
 
ValueCountFrequency (%) 
0330.2%
 
0.0010495382031< 0.1%
 
0.0010501995381< 0.1%
 
0.0010568590151< 0.1%
 
0.0010588733591< 0.1%
 
ValueCountFrequency (%) 
0.60847426481< 0.1%
 
0.60812317881< 0.1%
 
0.60799075241< 0.1%
 
0.60752608171< 0.1%
 
0.60748395671< 0.1%
 

mean_closeness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13699
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005908148564
Minimum6.518191387e-05
Maximum0.003373289354
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:24.209190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.518191387e-05
5-th percentile0.0001134520943
Q10.00016472718
median0.0003500128228
Q30.00062236653
95-th percentile0.002000550186
Maximum0.003373289354
Range0.00330810744
Interquartile range (IQR)0.0004576393501

Descriptive statistics

Standard deviation0.0006084340776
Coefficient of variation (CV)1.029821899
Kurtosis1.650645658
Mean0.0005908148564
Median Absolute Deviation (MAD)0.0002214302598
Skewness1.649788789
Sum8.095935978
Variance3.701920268e-07
MonotocityNot monotonic
2020-11-26T19:59:24.337312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8.821303762e-052< 0.1%
 
0.00010257633842< 0.1%
 
0.00010643490322< 0.1%
 
0.00010505724572< 0.1%
 
0.001886118831< 0.1%
 
0.00053211685351< 0.1%
 
0.00054227103361< 0.1%
 
0.0020555680741< 0.1%
 
0.00010308213391< 0.1%
 
0.00058299137361< 0.1%
 
Other values (13689)1368999.9%
 
ValueCountFrequency (%) 
6.518191387e-051< 0.1%
 
6.662907439e-051< 0.1%
 
6.705958217e-051< 0.1%
 
6.865560023e-051< 0.1%
 
6.974804513e-051< 0.1%
 
ValueCountFrequency (%) 
0.0033732893541< 0.1%
 
0.0032585712171< 0.1%
 
0.0032486038731< 0.1%
 
0.0032125540941< 0.1%
 
0.003178178791< 0.1%
 

mean_betweenness
Real number (ℝ≥0)

Distinct11988
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.35480376e-05
Minimum0
Maximum0.00367050182
Zeros1
Zeros (%)< 0.1%
Memory size107.1 KiB
2020-11-26T19:59:24.475412image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.326133315e-08
Q13.875270523e-08
median3.456921373e-07
Q37.214904809e-07
95-th percentile0.0002834931719
Maximum0.00367050182
Range0.00367050182
Interquartile range (IQR)6.827377757e-07

Descriptive statistics

Standard deviation0.0001569834597
Coefficient of variation (CV)3.604834302
Kurtosis129.7701919
Mean4.35480376e-05
Median Absolute Deviation (MAD)3.120396498e-07
Skewness9.121425338
Sum0.5967387593
Variance2.464380663e-08
MonotocityNot monotonic
2020-11-26T19:59:24.603686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.366464407e-08100.1%
 
2.621746929e-0880.1%
 
1.518293785e-0870.1%
 
1.521178447e-0870.1%
 
8.036414925e-0970.1%
 
1.014118965e-0870.1%
 
2.05458807e-086< 0.1%
 
1.313360891e-086< 0.1%
 
1.946777186e-086< 0.1%
 
3.09894319e-086< 0.1%
 
Other values (11978)1363399.5%
 
ValueCountFrequency (%) 
01< 0.1%
 
5.003793606e-101< 0.1%
 
5.013264509e-102< 0.1%
 
5.109301499e-101< 0.1%
 
1.004551866e-091< 0.1%
 
ValueCountFrequency (%) 
0.003670501821< 0.1%
 
0.0032190418721< 0.1%
 
0.0032092957341< 0.1%
 
0.0028187808161< 0.1%
 
0.0027895642681< 0.1%
 

assortativity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13701
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8308015712
Minimum0.4461152309
Maximum1
Zeros0
Zeros (%)0.0%
Memory size107.1 KiB
2020-11-26T19:59:24.920670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.4461152309
5-th percentile0.5068282155
Q10.8078689932
median0.8636668274
Q30.9396527637
95-th percentile0.9716369327
Maximum1
Range0.5538847691
Interquartile range (IQR)0.1317837705

Descriptive statistics

Standard deviation0.1441273849
Coefficient of variation (CV)0.1734799137
Kurtosis0.6438379083
Mean0.8308015712
Median Absolute Deviation (MAD)0.06513195912
Skewness-1.323601398
Sum11384.47393
Variance0.02077270307
MonotocityNot monotonic
2020-11-26T19:59:25.046047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.97859653062< 0.1%
 
0.98726706892< 0.1%
 
0.79514875791< 0.1%
 
0.78600969341< 0.1%
 
0.85886333481< 0.1%
 
0.88763089031< 0.1%
 
0.81454191181< 0.1%
 
0.80123802931< 0.1%
 
0.92714064181< 0.1%
 
0.94488780651< 0.1%
 
Other values (13691)1369199.9%
 
ValueCountFrequency (%) 
0.44611523091< 0.1%
 
0.45404553531< 0.1%
 
0.45534036861< 0.1%
 
0.45589748381< 0.1%
 
0.45882845831< 0.1%
 
ValueCountFrequency (%) 
11< 0.1%
 
0.99513573861< 0.1%
 
0.9948738441< 0.1%
 
0.99360890671< 0.1%
 
0.99305726861< 0.1%
 

Interactions

2020-11-26T19:59:12.675884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:12.799383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:12.907056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.009084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.115756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.219224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.324973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.434541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.544273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.743518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.852413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:13.966151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.075294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.198441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.331260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.449929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.566331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.683106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.792253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:14.896348image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.032685image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.135275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.243011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.347243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.452456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.562610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.687268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.803485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:15.911653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.024940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.141981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.254735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.364630image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.476747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.592415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.708550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.816817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:16.937587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.172854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.278564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.388674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.495435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.607057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.742888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.856402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:17.961945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.068750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.180979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.288077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.399684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.507945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.617476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.731631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.846117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:18.953075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.064449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.181074image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.293041image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.409708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.524450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.653785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.772920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:19.892332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.004217image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.116564image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.237096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.350435image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.467235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.581705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.696995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.816311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:20.935636image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.048131image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.290423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.416254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.537638image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.645803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.750513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.856624image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:21.967227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:22.077899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-11-26T19:59:25.161960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-26T19:59:25.315944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-26T19:59:25.469804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-26T19:59:25.625261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-26T19:59:22.273747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-11-26T19:59:22.522589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

rgmean_displacementshapedensityclique_numberaverage_clusteringmean_closenessmean_betweennessassortativity
00.4746180.0000000.0003060.00134330.0056680.0000835.003794e-100.994874
10.46473220.724373-0.0000090.00134720.0000000.0000825.080235e-090.961830
20.46164320.0798180.0000130.00135020.0000000.0000928.036415e-090.938803
30.45849022.1155860.0001870.00134920.0000000.0000883.019367e-090.967801
40.45644721.9988440.0001820.00135130.0048400.0000904.537639e-090.964951
50.45211222.7088640.0003280.00136630.0075810.0001061.818500e-080.962455
60.45089915.6693300.0002520.00134930.0033710.0000879.615861e-090.963636
70.44474124.6777520.0001650.00137030.0014730.0001101.008364e-080.941677
80.43793625.683807-0.0000020.00136730.0052710.0001045.577654e-090.952807
90.43653819.6904750.0001240.00138340.0062930.0001281.098756e-080.952529

Last rows

rgmean_displacementshapedensityclique_numberaverage_clusteringmean_closenessmean_betweennessassortativity
136930.4194521.066486-0.0015440.00142560.6031320.0017250.0001580.508937
136940.4195931.058365-0.0015910.00142770.6012940.0017610.0001640.512684
136950.4193461.048077-0.0015210.00142360.6010370.0017240.0001580.505704
136960.4196951.081644-0.0014820.00142760.6008510.0017570.0001810.521546
136970.4194671.040956-0.0015040.00142660.6008610.0017620.0001640.512302
136980.4195621.111165-0.0015830.00142360.6013930.0016560.0001290.497360
136990.4193781.070154-0.0015060.00142560.6010440.0016580.0001300.512344
137000.4194091.048686-0.0015160.00142960.6016880.0016710.0001240.502967
137010.4197771.034306-0.0014430.00142560.5997840.0016680.0001240.501561
137020.4191951.079562-0.0015240.00142660.6022020.0017270.0001580.505160